A path-space drift optimization model is approximated by Monte Carlo sampling, time discretization, and finite-dimensional subspaces, with a derived optimality gap bound that guides computational effort allocation.
Information projection on B anach spaces with applications to state independent KL -weighted optimal control
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Drift Optimization of Regulated Stochastic Models Using Sample Average Approximation
A path-space drift optimization model is approximated by Monte Carlo sampling, time discretization, and finite-dimensional subspaces, with a derived optimality gap bound that guides computational effort allocation.